The existing zoning collaborative control method cannot efficiently adapt to dynamic changes in traffic flow due to the fixed functional zoning of road segments. This study proposes the Dynamic Zoning COllaborative COntrol Method (DZCOM) for urban road traffic flow in a connected traffic environment. First, DZCOM dynamically divides the road segment into two functional zones, vehicle lane-changing and speed-adjustment zones, to integrate different driving behaviors. Second, to achieve rapid lane changes for multiple vehicles, a Multi-vehicle Cooperative Lane-changing Strategy based on Separate Lane-Speed Guidance (MCLS-SLSG) is designed, which rearranges the vehicles entering from the upstream intersection in longitudinal space, creating conditions for left- and right-turn vehicles to change lanes quickly. Finally, the trajectory of connected vehicles and the signal timing of intersections are jointly optimized based on the dynamic programming method to minimize average delay and ride comfort. The simulation results show that optimizing only the longitudinal trajectory of vehicles can reduce the average delay at intersections by 7.9% under moderate traffic volume. The joint optimization of vehicle trajectory and signal timing further reduced the average delay by 21.6%, demonstrating the effectiveness of DZCOM. Further research has shown that higher connected and automated vehicle penetration rates and speed limits can help reduce the average delay. Simultaneously, intersection spacing is another key factor restricting the operational effectiveness of the DZCOM; the method works best in the range of 400–600 m.

Activity tracking has the potential to promote active lifestyles among older adults. However, current activity tracking technologies may inadvertently perpetuate ageism by focusing on age-related health risks. Advocating for a personalized approach in activity tracking technology, we sought to understand what activities older adults find meaningful to track and the underlying values of those activities. We conducted a reflective interview study following a 7-day activity journaling with 13 participants. We identified various underlying values motivating participants to track activities they deemed meaningful. These values, whether competing or aligned, shape the desirability of activities. Older adults appreciate low-exertion activities, but they are difficult to track. We discuss how these activities can become central in designing activity tracking systems. Our research offers insights for creating value-driven, personalized activity trackers that resonate more fully with the meaningful activities of older adults.

In this article, we introduce a new sensor-based control method that regulates (by means of robot motion) the temperature of objects that are subject to a radiative heat source. This valuable sensorimotor capability is needed in many industrial, dermatology, and field robot applications, and it is an essential component for creating machines with advanced thermomotor intelligence. To this end, we derive a geometric-thermal-motor model, which describes the relation between the robot’s active configuration and the produced dynamic thermal response. We then use the model to guide the design of two new thermal servoing controllers (one model-based and one adaptive), and analyze their stability with Lyapunov theory. To validate our method, we report a detailed experimental study with a robotic manipulator conducting autonomous thermal servoing tasks. We show that the temperature of multiple objects with unknown thermophysical properties attached to the same end-effector can be effectively regulated by controlled robot motion. Although thermal sensing is a mature technology in many industrial thermal engineering applications, its use as a feedback signal for robot control has not been sufficiently studied in the literature. To the best of our knowledge, this is the first time that temperature regulation is formulated as a motion control problem for robots.

Clustering ensemble is one of the most important problems in ensemble learning. Though it has been extensively studied in the past decades, the existing methods often suffer from the issues like high computational complexity and the difficulty on understanding the consensus. In this paper, we study the more general soft clustering ensemble problem where each individual solution is a soft clustering. We connect it to the well-known discrete Wasserstein barycenter problem in geometry. Based on some novel geometric insights in high dimensions, we propose the sampling-based algorithms with provable quality guarantees. We also provide the systematical analysis on the consensus of our model. Finally, we conduct the experiments to evaluate our proposed algorithms.

BACKGROUND: Biomedical document triage is the foundation of biomedical information extraction, which is important to precision medicine. Recently, some neural networks-based methods have been proposed to classify biomedical documents automatically. In the biomedical domain, documents are often very long and often contain very complicated sentences. However, the current methods still find it difficult to capture important features across sentences. RESULTS: In this paper, we propose a hierarchical attention-based capsule model for biomedical document triage. The proposed model effectively employs hierarchical attention mechanism and capsule networks to capture valuable features across sentences and construct a final latent feature representation for a document. We evaluated our model on three public corpora. CONCLUSIONS: Experimental results showed that both hierarchical attention mechanism and capsule networks are helpful in biomedical document triage task. Our method proved itself highly competitive or superior compared with other state-of-the-art methods.